@InProceedings{LageCaPeBoTaLeLo:2009:SuVeLe,
               author = "Lage, Marcos and Castro, Rener and Petronetto, Fabiano and 
                         Bordignon, Alex and Tavares, Geovan and Lewiner, Thomas and Lopes, 
                         H{\'e}lio",
          affiliation = "Matm{\'{\i}}dia Laboratory – Department of Mathematics, PUC–Rio 
                         – Rio de Janeiro, Brazil and . and Matm{\'{\i}}dia Laboratory – 
                         Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil and 
                         Matm{\'{\i}}dia Laboratory – Department of Mathematics, PUC–Rio 
                         – Rio de Janeiro, Brazil and Matm{\'{\i}}dia Laboratory – 
                         Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil and 
                         Matm{\'{\i}}dia Laboratory – Department of Mathematics, PUC–Rio 
                         – Rio de Janeiro, Brazil and Matm{\'{\i}}dia Laboratory – 
                         Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil",
                title = "Support Vectors Learning for Vector Field Reconstruction",
            booktitle = "Proceedings...",
                 year = "2009",
               editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Vector Field, Support Vector Machine.",
             abstract = "Sampled vector \fields generally appear as measurements of 
                         real phenomena. They can be obtained by the use of a Particle 
                         Image Velocimetry acquisition device, or as the result of a 
                         physical simulation, such as a \fluid \flow 
                         simulation, among many examples. This paper proposes to formulate 
                         the unstructured vector \field reconstruction and 
                         approximation through Machine-Learning. The machine learns from 
                         the samples a global vector \field estimation function that 
                         could be evaluated at arbitrary points from the whole domain. 
                         Using an adaptation of the Support Vector Regression method for 
                         multi-scale analysis, the proposed method provides a global, 
                         analytical expression for the reconstructed vector \field 
                         through an ef\ficient non-linear optimization. Experiments 
                         on arti\ficial and real data show a statistically robust 
                         behavior of the proposed technique.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "11-14 Oct. 2009",
                  doi = "10.1109/SIBGRAPI.2009.20",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.20",
             language = "en",
                  ibi = "8JMKD3MGPBW4/35S5CTE",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW4/35S5CTE",
           targetfile = "57787_2.pdf",
        urlaccessdate = "2024, Apr. 29"
}